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Vehicular mobility scenarios are utilized to study vehicular networks and transportation systems. However, the generation of vehicular simulation scenarios at scale poses several research challenges. Large-scale vehicular datasets (in geographic coverage and time span) are not easily or publicly available, which hinders the generation of data-driven scenarios. In this paper, we introduce a systematic method, called En Route, to generate vehicular mobility scenarios from traffic datasets such as one derived from thousands of available traffic webcams covering major cities around the world. Our framework includes datadriven components for estimation of traffic density, flow, road occupancy, as well as origin-destination (O/D) matrix estimation, trip generation, and route/navigation calculations. By applying the framework, we explore the city of London using the dataset of ≈100 traffic cameras throughout the city. We …